Schedule planning is one of the most crucial issues for any airline company, because the profit of the company directly depends on the efficiency of the schedule. This paper presents a novel scheduling method which solves problems related to time scheduling, fleet assignment and maintenance routing simultaneously by Genetic Algorithms. Every schedule constraint is embeded in the fitness function, which is described as an object oriented model and works as a simulater developing itself over time, and whose solution (...) is executable without human correction. The schedular is able to solve the problems involving several hundred flights in a few hours, and the solutions are superior or equivalent to those by human experts in terms of the estimated profit. (shrink)

Crew Pairing is one of the most important and difficult problems for airline companies. Nets to fuel costs, the crew costs constitute the largest cost of airlines, and the crew costs depend on the quality of the solution to the pairing problem. Conventional systems have been used to solve a daily model, which handles only regular flights with many simplifications, so a lot of corrections are needed to get a feasible solution and the quality of the solution is not so (...) high. A fully dated model, which handles regular flights and irregular flights simultaneously, is extremely hard and has not been solved directly hitherto. The number of irregular flights tend to increase in Europe and Japan, hence the resolution of the practical fully dated pairing system is desired. This paper presents a new approach which solves directly the fully dated crew pairing by Genetic Algorithms. GA is improved with stochastic processes to attack the fully dated model, and many heuristics are included in the decoding mecanism of the GA. For several hundred flights per day for fully dated 2 months scheduling period, the system found better or equivalent solutions to the human scheduler’s without any simplification from 5 to 20 times faster. (shrink)

This study aims to develop a hybrid model to represent the human mind from a functionalist point of view that can be adapted to artificial intelligence. The model is not a realistic theory of the neural network of the brain but an instrumentalist AI model, which means that there can be some other representative models too. It had been thought that the provability of an axiomatic system requires the completeness of a formal system. However, Gödel proved that no consistent formal (...) system can prove its own consistency. There is a paradoxical limit to provability. Both consistency and completeness are impossible together. These formal limits form the basis of our model: the hybrid model of the mind consists of a formal axiomatic system and an evolutionary algorithm (EA) of intelligence, which also includes the genetic algorithm (GA) of consciousness. The GA of consciousness operates based on intentions, functioning in conjunction with the principles of EA. This collaboration allows it to transcend the paradoxical formal limits of intelligence. However, GA overcomes the problem of paradoxicality at the cost of producing illusions. Following this collaboration gives us a GA operating system. After all, if the rational optimization task of the GA in question produces illusions, then the rationality must lie elsewhere in the paradox. (shrink)

The Vehicle Routing Problem (VRP) has been extensively studied by different researchers from all over the world in recent years. Multiple solutions have been proposed for different variations of the problem, such as Capacitive Vehicle Routing Problem (CVRP), Vehicle Routing Problem with Time Windows (VRP-TW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), among others, all of them with deterministic times. In the last years, researchers have been interested in including in their different models the variations that travel times may (...) experience when exposed to all kind of phenomena, mainly vehicle traffic. This article addresses the VRP from this perspective, proposing the design and implementation of a genetic algorithm based on neutrosophic theory for calculating the fitness function of each route, considering the variability and uncertainty present in travel times. A deterministic genetic algorithm is also implemented with the average travel times to compare it with the neutrosophic algorithm using simulation. As conclusion, a deterministic algorithm does not necessarily generate the best solution in the real world, full of uncertainty. Also, the quantification of uncertainty using neutrosophic theory can be used in route planning, opening a broad and interesting field of research for future investigations. (shrink)

This article presents the design and development of a genetic algorithm to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

Shane Harwood and Richard Scheines. Genetic Algorithm Search Over Causal Models.

The paper discusses the problem of assignment production resources in executing a production order on the example of the car rims manufacturing process. The more resources are involved in implementing the manufacturing process and the more they can be used interchangeably, the more complex and problematic the scheduling process becomes. Special attention is paid to the effective scheduling and assignment of rim machining operations to production stations in the considered manufacturing process. In this case, the use of traditional scheduling methods (...) based on simple calculations, or the know-how of process engineers often turns out to be insufficient to achieve the intended results. Due to the scale of the problems faced in practice, the methods based on approximate approaches (Genetic and Tabu Search) were used to solve them. In this perspective, the problem under consideration involves the extension of the classic assignment problem with the possibility of taking into account: the times of operations, potential changeovers and the capacity of production resources. (shrink)

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will (...) converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results. (shrink)

Due to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the (...) performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets. (shrink)

The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued (...) with the GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances. (shrink)

Automated Guided Vehicles (AGV) are an essential element of transport in industry 4.0. Although they may seem simple systems in terms of their kinemat.

Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the (...) reason for why evolutionary designs are often hard to understand is that they exhibit non-modular functionality, and that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation. (shrink)

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. (...) Here we focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of "boom and bust." In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of "boom and bust." These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology. (shrink)

The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far (...) in the field. It is also important to analyse the applicability of the premise of the dominance techniques applied so far in order to implement the technique. The work presents a systematic review of diploid genetic algorithms, examines the premise of the dominance relation used in different works and discusses the future scope. The work also discusses the biological basis of evaluating dominance. The work is important as the future of machine learning relies on techniques that are robust as well as efficient. (shrink)

We extend previous work by modeling evolution of communication using a spatialized genetic algorithm which recombines strategies purely locally. Here cellular automata are used as a spatialized environment in which individuals gain points by capturing drifting food items and are 'harmed' if they fail to hide from migrating predators. Our individuals are capable of making one of two arbitrary sounds, heard only locally by their immediate neighbors. They can respond to sounds from their neighbors by opening their mouths or (...) by hiding. By opening their mouths in the presence of food they maximize gains; by hiding when a predator is present they minimize losses. We consider the result a 'natural' template for benefits from communication; unlike a range of other studies, it is here only the recipient of communicated information that immediately benefits. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

In this paper, I propose a populational schema of modeling that consists of: a linear AFSV schema, and a higher-level schema employing the genetic algorithm. The basic ideas of the proposed solution are as follows: whole populations of models are considered at subsequent stages of the modeling process, successive populations are subjected to the activity of genetic operators and undergo selection procedures, the basis for selection is the evaluation function of the genetic algorithm. The schema can be (...) applied to automate the modeling of the mind/brain by means of artificial neural networks: the structure of each network is modified by genetic operators, modified networks undergo a learning cycle, and successive populations of networks are verified during the selection procedure. The whole process can be automated only partially, because it is the researcher who defines the evaluation function of the genetic algorithm. (shrink)

The well-known classifier support vector machine has many parameters associated with its various kernel functions. The radial basis function kernel, being the most preferred kernel, has two parameters to be optimized. The problem of optimizing these parameter values is called model selection in the literature, and its results strongly influence the performance of the classifier. Another factor that affects the classification performance of a classifier is the feature subset. Both these factors are interdependent and must be dealt with simultaneously. Following (...) the multiobjective definition of feature selection, we have applied a multiobjective genetic algorithm, NSGA II, to optimize the feature subset and model parameters simultaneously. Comparison of the proposed approach with the grid algorithm and GA-based method suggests that the MOGA-based approach performs better than the grid algorithm and is as good as the GA-based approach. Moreover, it provides multiple solutions instead of a single solution. The users can prefer one feature subset over the other as per their requirement and available resources. (shrink)

The incorporation of hybrid systems based on renewable sources for the optimization of electricity distribution systems and planning of power supply strategies using genetic algorithms is studied. A series of characteristics of electrical sub-stations was chosen and through simulations, data were obtained for the optimization of the existing infrastructure, which provides reliability, security, economic supply and quality of service. An algorithm was obtained with the optimal configuration of various components: photovoltaic panels, batteries, AC generator, fuel cell and inverter, (...) which in the case of mono-objective optimization, minimized total system costs throughout its useful life. The most appropriate control strategy or combination of control variables was obtained to minimize costs. Keywords: Electrical distribution, Hybrid systems, Genetic algorithms. References [1]D. F. Pinzón, «Diseño óptimo de Sistemas de Distribución,» Universidad Nacional de Colombia, Bogotá, 2014. [2]F. A. Mendoza Lameda, «Diseño multiobjetivo y multietapa de sistemas de distribución de energía eléctrica aplicando algoritmos evolutivos,» Universidad de Zaragoza, Zaragoza, 2010. [3]L. Miró Hernández and R. Vizcón Toledo, «Sistema Hibrido Propuesto Para la Generación de Electricidad en un Policlínico,» Revista Avanzada Científica, vol. 9, nº 2, pp. 50-56, 2006. [4]J. Lagunas M., C. Ortega S. and P. Caratozzolo M., «Control supervisorio para sistemas híbridos de geración eléctrica basado en lógica difusa,» Boletin UE, Monterrey, 2005. [5]J. L. Bernal Agustín, «Aplicación de algoritmos genéticos al diseño óptimo de sistemas de distribución de energía eléctrica,» Universidad de Zaragoza, Zaragoza, 1998. [6]V. MIranda, J. V. Ranito and L. Proenca, «Genetic Algorithms in Optimal Multistage Distribution Network Planning, » IEEE, Porto, 1994. [7]I. Ramirez-Rosado and J. Dominguez-Navarro, «Computer Aided Desing of Power Distribution Systems: Multiobjective Mathematical Simulations» International Journal of Power and Energy Systems, vol. 19, nº 4, pp. 1801-1810, 2004. (shrink)

In this study a combination of computer tools for coupling and virtual screening is detailed, in 108 active molecules and 3620 decoys to find stabilizers for G quadruplex. To have more precise results, combinations of coupling programs with fifteen energy scoring functions were applied. The validation and evaluation of the metrics was done with the CompScore genetic algorithm. The results showed an increase in BEDROC and EF of 50% compared to other strategies, as well as reflecting early recognition of (...) active molecules. From these results it is possible to work with the molecules that showed a good early recognition and evaluate their effect as G4 stabilizers. This ensures more efficient and accurate results in the preclinical stage for the development of anticancer drugs. Keywords: Enrichment metrics; telomere; G quadruplex ; CompScore. References [1]M. Porru, P. Zizza, M. Franceschin, C. Leonetti and A. Biroccio. «EMICORON: A multi-targeting G4 ligand with a promising preclinical profile» 2017. Biochimica et Biophysica Acta - General Subjects, 1861, 1362–1370. [Online]. Available: https://doi.org/10.1007/s00294-018-0836-6. [2]K. Tomita. «How long does telomerase extend telomeres? Regulation of telomerase release and telomere length homeostasis». Current Genetics, 64, 1177–1181. 2018. [Online]. Available: https://doi.org/10.1016/j.bbagen. 2016.11.010. [3]M. Jafri, S. Ansari, M. Alqahtani and J. Shay. «Roles of telomeres and telomerase in cancer, and advances in telomerase-targeted therapies. Genome Medicine., 2016. [Online]. Available: https://doi.org/10.3390/ijms19020482. [4]J. Huppert and S. Balasubramanian. «G-quadruplexes in promoters throughout the human genome». 35, 406–413. 2007. [Online]. Available: https://doi.org/10.1016/j.bbapap.2017.06.012. [5]S. Joy, Vijayakumar, S. Choi and H. Sunhye. «Role of computer-aided drug design in modern drug discovery». Archives of Pharmacal Research. 2015. [Online]. Available: https://doi.org/10.1007/s12272-015-0640-5. [6]S. Asamitsu, S. Obata, Z. Yu, T. Bando and H. Sugiyama. «Recent Progress of Targeted G-Quadruplex-Preferred Ligands Toward Cancer Therapy». Molecules, 24, 429. 2019. [En línea]. Available: https://doi.org/10.3390/molecules24030429. [7]R. Monsen and J. Trent. «Biochimie G-quadruplex virtual drug screening : A review». Biochimie, 152, 134–148. 2018. [Online]. Available: https://doi.org/10.1039/c9cc06748e. [8]J. Beauvarlet, P. Bensadoun, E. Darbo, G. Labrunie, E. Richard, I. Draskovic and M. Djavaheri-mergny. «Modulation of the ATM / autophagy pathway by a G-quadruplex ligand tips the balance between senescence and apoptosis in cancer cells». 1–18. 2019. [Online]. Available: https://doi.org/10.1093/nar/gkz095. [9].Z. Crees, J. Girard, Z. Rios, G. Botting, K. Harrington and C. Shearrow. « Oligonucleotides and G-quadruplex stabilizers: targeting telomeres and telomerase in cancer therapy».2014. [Online]. Available: https://doi.org/10.2174/1381612820666140630100702. [10].M. Meier, A. Moya-torres, N. Krahn, M. Mcdougall, L. Orriss, E. Mcrae and T. Patel. «Structure and hydrodynamics of a DNA Gquadruplex with a cytosine bulge»., 1–13. 2018. [Online]. Available: https://doi.org/10.1093/nar/gky307. (shrink)

In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of (...) large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature. (shrink)

Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.

The topology optimization of computer communication network is studied based on improved genetic algorithm, a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication network reliability model, respectively. (...) It has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently. (shrink)